Spring 2017: BUSA 3110 – Statistics for Business Days 1 and 2 Kim I. Melton, Ph.D.

Logistics Roll Information for the first two weeks posted at: faculty.ung.edu/kmelton/busa3110.html Syllabus (Brief and Full online) Homework Slides for Week 1 and Module 1 During the second week, all of my sections will be combined in D2L under Section DA.

Materials That You Need to Purchase $$ Custom packet (available at Dahlonega and Gainesville Bookstores) Includes: Selected chapters from Business Statistics, 3rd Edition by Sharpe, De Veaux, and Velleman Access to MyStatLab with Homework management system Chapter quizzes Complete text of Business Statistics, 3rd edition Answers to odd problems Data for problems in the text And more

Other Support Materials (Links are available on the Homework page) D2L All of my sections will be combined into Section DA after Drop/Add

Learning Expectations for Class Attendance Arrive on time Stay the entire time Preparation Spend time before class reading (text and homework) Take notes while in class Work homework after class Professionalism Take responsibility for learning Believe you can learn statistics Ask questions Try to answer questions Seek help EARLY when you are struggling Be ethical Put phones away INVEST

Course Description A second course in statistical methods with special orientation to applications in business. Emphasis will be placed on application of statistical techniques, assessing their appropriateness, and communicating results to various audiences. Topics include: data collection, sampling, data visualization, data analysis, model building using regression, and other statistical techniques. Statistical software is used extensively in the course. This course should be taken as soon as the prerequisite is satisfied. Prerec: Admission to Upper Division and MATH 2400 with a grade of C or higher.

Learning Outcomes (Course Level) Upon completion of this course, students should be able to: select appropriate statistical methods to guide decision- making generate and use statistical output to analyze data identify the limitations of the statistical methods covered communicate how statistical studies were conducted and the results of those studies recognize ethical issues related to the collection and analysis of data and the communication of the results of the analysis

Statistics Applied The Course Uses data From situations where variation exists In quantitative models To guide decisions That inform action Applied For use in a practical setting Where theoretical assumptions may not apply perfectly And results and limitations need to be communicated in the language of the situation

Communicate Results (or revise theory) Variation exists in all processes Develop Theories Design Data Collection Plan Improvement comes from reducing variation Select Statistical Tool(s) for Analysis Statistical Thinking Statistics Collect Data Processes are inter-connected “Clean” Data Analyze Data (including relationships between variables) Optimization of the parts does not optimize the whole Assess Assumptions Communicate Results (or revise theory) Draw Statistical Conclusions

Example Ad: “Real pork is the first ingredient.” What does the order of ingredients mean? Would it be possible that a product that lists a specific ingredient first has less of that ingredient than a competing product that has some other ingredient first? Ingredients lists copied from petsmart.com

Data/Information/Knowledge/Wisdom Evaluates knowledge/understanding; deals with values; uses judgment; answers what is best and why Doing the right things (Effectiveness) WISDOM Explains; provides answers to how to and why questions KNOWLEDGE/ UNDERSTANDING Doing things right (Efficiency) Describes; provides answers to who, what, where, and when questions INFORMATION Symbols (raw values) that represent properties of objects/events DATA Based on the work of Russell Ackoff. See “From Data to Wisdom” in Ackoff’s Best, pp. 170-172, 1999.

Data/Information/Knowledge/Wisdom (in Statistics – example: Earned Hours) 126 113 96 106 Information This is for 5 students in a section of BUSA 3110 (a junior level class) collected on Jan. 5, 2017. Summary measures: Average: 110.8 [2nd semester Senior] Range: 30 Median: 113 St. Dev.: 10.986 Knowledge The data came from a sample—so summary measures are statistics X , R, X and s Understanding Likely, we would want to infer to the population (and estimate the associated parameters) m s (Range not usually reference for pop.) Wisdom Q: How was the sample selected? A: The first 5 registrations on the 1st day of registration Assumptions for inference? Random sampling From a single population (i.e., homogeneous – where order the data were created is irrelevant) Implication?

Analysis for the Full Population – i. e Analysis for the Full Population – i.e., All students in the section of BUSA 3110

Day 1: Homework Go to faculty.ung.edu/kmelton/BUSA3110.html Read the complete syllabus Complete the homework assignment listed for January 9 Come to NOC 017 for class on Wednesday [We won’t go to the lab until next week] Links (copy and paste): JMP: Software.ung.edu UNG Virtual Lab: https://my.ung.edu/departments/information- technology/Pages/Remote-Access.aspx Microsoft Office (right side of the page): https://my.ung.edu/departments/information-technology/Pages/Office- 365.aspx MyStatLab: mystatlab.com

Syllabus Text, MyStatLab, JMP, D2L, MS Office Accessing material D2L and MyStatLab Software Availability: JMP and MS Office Course Format Grading General expectations (especially deadlines, make-ups, extra credit, academic integrity, phones) Inclement Weather

Format & General Expectations Learning is not a divided responsibility (not I teach, you learn)— learning is a joint responsibility (we learn together) My “hot buttons” Timeliness Ethical behavior Professional orientation toward learning This includes putting phones away and engaging in class Recognition that “true” learning involves more than getting the right answer

Grading MyStatLab Homework (14 points) MyStatLab Quizzes (14 points) 90 and above A 80 – 89 B 70 – 79 C 60 – 69 D Below 60 F MyStatLab Homework (14 points) Drop the lowest two and average the rest [then take percent of 14] MyStatLab Quizzes (14 points) Average all [then take percent of 14] Instructor Supplied Assignments (63 points) Seven assignments each graded out of 9 [add them up] Preparation / Engagement (15 points) Total earned/total available [then take percent of 15] Pre-final grade = Add the points from each section Final (0-18 points) Two problems each out of nine points [treated as Instructor Supplied Assignments 8 and 9] Final Grade = Points from (HW + Quizzes + Preparation / Engagement + 7 Highest Instructor Supplied Assignments)

Instructor Supplied Assignment Topics (Tentative List) Fundamentals of using JMP Summarizing Data Collecting “Good” Data for Statistical Inference Simple Linear Regression Equations, Graphs, Model Statements, Hypotheses Multiple Regression and Testing Theories Model Building and Selecting the “Best” Model

A Word about Deadlines (MyStatLab and D2L) Deadlines are set to: Allow you time to see assignments well before the due date For MyStatLab assignments you should make your first attempt as soon as the assignment becomes available Allow you time to complete the assignments after the material is covered Provide you with as much time as possible prior to when I will start grading Therefore, many deadlines will be set on non-class days Remember, you can submit assignments before the deadline

Organizational Mechanics to Course Content. Statistics. Past. Present Organizational Mechanics to Course Content Statistics Past Present Future

The Historical Role of Data in Statistics Describe (Descriptive Statistics) Summarizes data Graphically Through formulas and tables Infer (Inferential Statistics) Use data from a small number of observations to draw conclusions about the larger group Improve (Process Studies) Use data from past experience to help predict expected outcomes at a different time or place or to direct action to influence future outcomes

How (and Why) is the Field of Statistics Changing? Think “Data” Source: http://www.datasciencecentral.com/profiles/blogs/data-veracity

The Evolving Role of Data in Statistics Descriptive/Informative Includes current descriptive and inferential statistics Looks at past and current performance to “describe” Predictive/Explanatory Looks at past and current performance with a goal of predicting future performance (i.e., to be able to “explain”) Addresses “what if” questions Prescriptive/Understanding of Interactions & Implications Uses quantitative models to assess how to operate in order to achieve some objective within constraints (and may include deterministic and probabilistic aspects)

Analytics

What do we mean by “good data”? Considerations when you are collecting data Considerations when you are evaluating reports that claim to be based on data

Day 2: Homework Go to faculty.ung.edu/kmelton/BUSA3110.html Complete the homework assignment listed for January 11 Reminder: You should have your MyStatLab account set up by now; and your first MyStatLab Homework is due this weekend If you want to get a head start on loading JMP on your computer, the information is posted with Friday’s assignment and at software.ung.edu